humanwhocodes/computer-science-in-javascript

★ 9,120JavaScriptAudience · developerComplexity · 1/5LicenseSetup · easy

Mindmap

mindmap
  root((repo))
    What it does
      Data structures
      Sorting algorithms
      Encoding examples
    Content layers
      Original 2009 code
      Updated 2019 code
      Blog post pairs
    Audience
      JS beginners
      CS learners
    Setup
      Node.js required
      Run and test

mindmap root((repo)) What it does Data structures Sorting algorithms Encoding examples Content layers Original 2009 code Updated 2019 code Blog post pairs Audience JS beginners CS learners Setup Node.js required Run and test

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Things people build with this

USE CASE 1

Learn how linked lists, binary search trees, and sorting algorithms work by reading plain, step-by-step JavaScript code.

USE CASE 2

Use the implementations as study material alongside the author's blog posts that explain the reasoning behind each one.

USE CASE 3

Run the included tests to verify your understanding as you work through each data structure or algorithm.

Tech stack

JavaScriptNode.js

Getting it running

Difficulty · easy Time to first run · 5min

MIT license, use freely for any purpose, including commercial use, as long as you keep the copyright notice.

In plain English

This repository is a companion to a series of educational blog posts written by Nicholas C. Zakas, a well-known JavaScript author. It contains JavaScript implementations of classic computer science concepts: data structures like linked lists and binary search trees, sorting algorithms like bubble sort and selection sort, and other foundational algorithms like binary search and base64 encoding. The purpose is educational. The code is deliberately written to be easy to follow rather than as concise as possible, and the README notes that heavy commenting is intentional to help people who are new to these concepts. Each implementation pairs with a blog post on the author's website that walks through the reasoning behind it. The repository has two layers of content. The original implementations go back to a 2009 blog post series and sit in folders named data-structures, algorithms, and encodings. A newer batch of implementations, started in 2019 and updated to use modern JavaScript features, lives in the src folder. The author is gradually replacing the old versions with updated ones, so the two sets coexist during that transition. Running the code requires Node.js version 8 or later. Setup is the standard pattern for a JavaScript project: clone the repository, run the install command to pull in dependencies, then run the test command to verify everything works. The project is maintained as a teaching resource tied to the blog post series, so only bug fixes are accepted. No new features are being added. The license is MIT, which allows free use for any purpose. Anyone learning JavaScript who wants to see how these fundamental data structures and algorithms work, with clear step-by-step code and accompanying written explanations, will find this useful.

Copy-paste prompts

Prompt 1

Walk me through the linked list implementation in humanwhocodes/computer-science-in-javascript and explain each method step by step.

Prompt 2

Using this JavaScript binary search tree as a guide, help me build my own BST with insert, search, and delete methods.

Prompt 3

Compare the bubble sort and selection sort implementations in this repo and explain when each one is faster.

Prompt 4

Help me understand how base64 encoding works using the JavaScript implementation in this repository.

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